Data, lots of it, may detect who will have dementia and what type

July 6, 2020

What if, by inputting information about dementia and a patient into a supercomputer and crunching the data, a treatment team could quantify the individual’s risk of developing the disease?

And what if, based on the results, the team could diagnose which type and subtype of dementia that the patient might develop?

And what if the team could prescribe lifestyle changes and drug therapy specifically geared for that subtype to prevent it?

That’s the ambitious vision of the future held by Mohamad Habes, PhD, director of the Glenn Biggs Institute neuroimaging core at UT Health San Antonio.

Dr. Habes coauthored a paper published this year in the journal Biological Psychiatry that reviewed 29 studies of subtypes of multiple neurodegenerative diseases. These included Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia and Lewy body dementia. The team also reviewed studies of general brain aging not associated with a disease diagnosis.

Clustering information to increase diagnostic power

“In this paper, what we show is that there are atypical presentations of early Alzheimer’s and other diseases,” Dr. Habes said. “If a patient is at risk to develop a disease, and we use MRI (magnetic resonance imaging) and PET (positron emission tomography) to image that patient, we can then utilize high-dimensional clustering to group the patient into a specific subtype.”

The MRI and PET technologies have existed for decades, but the data that can now be gleaned from each image is greater and computing power is commensurately higher. This is an age of machine learning and artificial intelligence. High-dimensional clustering is the concept of bringing multiple dimensions of information together to compute a patient’s disease state.

“Just think of two dimensions of a picture, extending to three dimensions, and then to more and more,” Dr. Habes said. “By doing that, you have a better insight, a better representation of the problem, when you have more information about it.”

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To read the full article, visit UT Health San Antonio Newsroom.

Article Categories: News, Research